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Global Positioning System (GPS) and Inertial Navigation System (INS) technologies have been widely used in a variety of positioning and navigation applications. Both Systems have their unique features and shortcomings. Hence, combined system of GPS and INS can exhibit the robustness, higher bandwidth and better noise characteristics of the inertial system with the long-term stability of GPS, Integrated together are used to provide a reliable Navigation System. This paperwill compare the performance of Kalman filter and Dynamic adaptive neuro fuzzy system for integrated INS/GPS systems. The Simulation Results by Matlab7 Programming Language showed great improvements in positioning, gives a best results and reduce the root mean square error (r.m.s.) when used Dynamic adaptive neuro fuzzy system rather than Kalman filter.

Neural networks are complex nonlinear models;this characteristic enables them to be used in nonlinear system modeling and prediction applications.The estimation and prediction are importantroles in the communication system.The proposed approach based onthe Wavelet Neural Networks (WNNs)usesmorlet as an activation function in thehidden layer of the wavelet neural network,while the Radial Basis Function Neural Networks (RBFNNs)usebasis functionthat can be calculated as a Gaussian function. In this paper,a comparisonbetween the performance ofWavelet Neural Networksand Radial Basis Functionfor GPS prediction is presented.The comparison results(usingMATLAB programming)presentthat the Wavelet Neural Networks method has a great approximation ability, suitability and more stable in Global Positioning System (GPS)prediction than the Radial Basis Function Neural Networks,were highly effective predictions for accurate positioning and RMS errors are 0.05meter after using of Wavelet Neural Networks prediction.

A Pico cell is a small, low-cost, fast coverage and extra capacity which can be used wherever the signal drops. The low cost of these cells and the easy installation as compared with BTSs is very important feature, one more feature is that Pico cells are more effective in dense phone usage areas and at the extremely high buildings, also as its radiation power is much less than BTSs so it has no health effects on human.This paper makes a design to support the network by using the technique of Pico cells in order to improve the signal quality inside dense phone usage areas and take multiple scenarios to show the importance of using such type of cells. The results show the advantages of using this cell to improve the signal quality especially in dense phone usage areas.